Unranked Competition on Kaggle held by ITI AI-Pro.

You are given a set of features extracted from the shape of the beans in images and it's required to predict the type of each bean. There are 7 bean types in this dataset.

The dataset consists of features describing the shape of the bean and you're required to predict it's type.

• ID - an ID for this instance
• Area - (A), The area of a bean zone and the number of pixels within its boundaries.
• Perimeter - (P), Bean circumference is defined as the length of its border.
• MajorAxisLength - (L), The distance between the ends of the longest line that can be drawn from a bean.
• MinorAxisLength - (l), The longest line that can be drawn from the bean while standing perpendicular to the main axis.
• AspectRatio - (K), Defines the relationship between L and l.
• Eccentricity - (Ec), Eccentricity of the ellipse having the same moments as the region.
• ConvexArea - (C), Number of pixels in the smallest convex polygon that can contain the area of a bean seed.
• EquivDiameter - (Ed), The diameter of a circle having the same area as a bean seed area.
• Extent - (Ex), The ratio of the pixels in the bounding box to the bean area.
• Solidity - (S), Also known as convexity. The ratio of the pixels in the convex shell to those found in beans.
• Roundness - (R), Calculated with the following formula: (4piA)/(P^2)
• Compactness - (CO), Measures the roundness of an object: Ed/L
• ShapeFactor1 - (SF1)
• ShapeFactor2 - (SF2)
• ShapeFactor3 - (SF3)
• ShapeFactor4 - (SF4)
• y - the class of the bean. It can be any of BARBUNYA, SIRA, HOROZ, DERMASON, CALI, BOMBAY, and SEKER.

• SciKitLearn:
  • Models: SVC, TSNE
  • Metrics: classification_report, accuracy_score, average_precision_score, f1_score
  • Preprocessing: LabelEncoder, RobustScaler
  • Model Selection: train_test_split
• NumPy
• Pandas
• SciPy
• Visualization:
  • matplotlib.pyplot
  • seaborn
  • plotly

• Observations:
  • Training: 10834
  • Test: 2709
• Features: 16

• Numeric Analysis (Duplicates, Null Values, Skewness, etc.)
• Visual Analysis (Boxplots, Histograms, Heatmaps, TSNE, etc.)

• Training \ Validation Split
• Normalization

Model Chosen: Support Vector Classifier

Metrics Used:

• F1 Score
• Recall
• Precision

• AbdElrahman Eid
• Ahmed Azzam
• Ahmed Zakaria
• Mohamed Nasser
• Mostafa Nafie